At present moment, usage of PrePostprocessor (PPP) is officially supported only for Windows. However, sometimes it is necessary to run PPP on Linux too. Actually, customers of FlowVision have such possibility due to special software for running of windows applications on Linux, like Wine. Let us consider, how to run PPP on Linux using Wine, on the example of Ubuntu 16.04.1, and you will be convinced how easily it is.

PPP on Linux

Too quick changing of variable can give divergence or make convergence worse, because fast changes will give large gradients of physical variables.

For example, in tasks about turbines and compressors we have rotor which has large velocity of rotation. When we start simulation we can’t specify initial velocity for gas between blades of rotor, because structure of flow too complex. It means that during first time step we will have quick changes of velocity from zero (initial velocity of gas) to some large velocity of blades.


If we will solve this task for incompressible liquid, we will have divergence. It is possible to make convergence better if we will exclude too quick changes, for example, we can change speed of rotation smoothly from zero.

FlowVision allow to specify very complex equations for any user’s variables. Below you will find some formula template which useful to use every time when you need specify some smooth changing of variable.


In this article you will read about parallelization in FlowVision. It is necessary to understand several features, if you want to get a maximum from advantages of parallel simulations.

  • Why is not possible to accelerate simulation infinitely?
  • What is role of count of computational and initial cells in parallel simulations?
  • How to be faster?
  • What hardware is necessary to prefer?

How parallelization works. Computational grid decomposition

When you start simulation, FlowVision first of all will build computational grid and split it for several parts. After this FlowVision will redistribute parts of computational grid between processors. On picture below you can see result of splitting of computational grid for 4 processors:



On each processor will be run one copy of Solver which will solve own part of computational grid.

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